
"""simple docstring"""

from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_            =    TypeVar('''T''')
class   _UpperCAmelCase				(		Generic[T]				):




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       data
          UpperCAmelCase          =       self
          UpperCAmelCase          =       0

class   _UpperCAmelCase				(		Generic[T]				):




      def __init__(				self				)    ->       None:
          # map from node name to the node object
          UpperCAmelCase          =       {}




      def      a_   (				self ,							lowercase_				)    ->       None:
          # create a new set with x as its member
          UpperCAmelCase          =       DisjointSetTreeNode(SCREAMING_SNAKE_CASE_				)




      def      a_   (				self ,							lowercase_				)    ->       DisjointSetTreeNode[T]:
          # find the set x belongs to (with path-compression)
          UpperCAmelCase          =       self.map[data]
          if elem_ref != elem_ref.parent:
              UpperCAmelCase          =       self.find_set(elem_ref.parent.data				)
          return elem_ref.parent




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       None:
          # helper function for union operation
          if nodea.rank > nodea.rank:
              UpperCAmelCase          =       nodea
          else:
              UpperCAmelCase          =       nodea
              if nodea.rank == nodea.rank:
                  nodea.rank += 1




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       None:
          # merge 2 disjoint sets
          self.link(self.find_set(SCREAMING_SNAKE_CASE_				) ,							self.find_set(SCREAMING_SNAKE_CASE_				)				)

class   _UpperCAmelCase				(		Generic[T]				):




      def __init__(				self				)    ->       None:
          # connections: map from the node to the neighbouring nodes (with weights)
          UpperCAmelCase          =       {}




      def      a_   (				self ,							lowercase_				)    ->       None:
          # add a node ONLY if its not present in the graph
          if node not in self.connections:
              UpperCAmelCase          =       {}




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       None:
          # add an edge with the given weight
          self.add_node(SCREAMING_SNAKE_CASE_				)
          self.add_node(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       weight
          UpperCAmelCase          =       weight




      def      a_   (				self				)    ->       GraphUndirectedWeighted[T]:
          UpperCAmelCase          =       []
          UpperCAmelCase          =       set()
          for start in self.connections:
              for end in self.connections[start]:
                  if (start, end) not in seen:
                      seen.add((end, start)				)
                      edges.append((start, end, self.connections[start][end])				)
          edges.sort(key=lambda lowercase_				: x[2]				)

          # creating the disjoint set
          UpperCAmelCase          =       DisjointSetTree[T]()
          for node in self.connections:
              disjoint_set.make_set(SCREAMING_SNAKE_CASE_				)

          # MST generation
          UpperCAmelCase          =       0
          UpperCAmelCase          =       0
          UpperCAmelCase          =       GraphUndirectedWeighted[T]()
          while num_edges < len(self.connections				) - 1:
              UpperCAmelCase          =       edges[index]
              index += 1
              UpperCAmelCase          =       disjoint_set.find_set(SCREAMING_SNAKE_CASE_				)
              UpperCAmelCase          =       disjoint_set.find_set(SCREAMING_SNAKE_CASE_				)
              if parent_u != parent_v:
                  num_edges += 1
                  graph.add_edge(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_				)
                  disjoint_set.union(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_				)
          return graph








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      :  Dict			     =			{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Any			     =			['LxmertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'LxmertEncoder',
	    'LxmertForPreTraining',
	    'LxmertForQuestionAnswering',
	    'LxmertModel',
	    'LxmertPreTrainedModel',
	    'LxmertVisualFeatureEncoder',
	    'LxmertXLayer',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLxmertForPreTraining',
	    'TFLxmertMainLayer',
	    'TFLxmertModel',
	    'TFLxmertPreTrainedModel',
	    'TFLxmertVisualFeatureEncoder',
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	a      :  str			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






